ParentChecker
Introduction:
ParentChecker is a user-friendly tool that uses the segregation patterns of progeny to infer missing genotype information of parent lines that have been used to construct mapping populations. It can also be used to automate correction of linkage phase errors in genotypic data that is in ABH format.
Download: ParentChecker.zip; Marker generator.xlsx
Support: You are invited to join our new user group. If your are a user of ParentChecker or PROC QTL, please submit your email address below.
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PROC QTL
Pub-Date: January 29, 2012
Introduction:
PROC QTL is a user defined SAS procedure for mapping quantitative trait loci (QTL). The program was coded in C++ and the interface with the SAS system was conducted using the SAS/Toolkit software (SAS 9.2, 2008). Since this procedure is not a built-in SAS procedure, users need to obtain a copy of the executable file of PROC QTL and install the software in their personal computers before PROC QTL can be executed. Of course, users need a regular SAS license prior to the installation of PROC QTL. Once PROC QTL is installed, users can call the procedure just like they call any other regular SAS procedures without noticing the differences between this customized procedure and other built-in SAS procedures.
Updates:
The divided-by-zero error and the floating-overflow error in the Bayesian analysis have been fixed.
The new package is compatible with SAS9.2/64bit and SAS9.3/64bit.
Version 2.0:
A book entitled "Principles and Procedures of QTL Mapping" has been added to the details of the QTL manual.
Correct citation:
Zhiqiu Hu and Shizhong Xu (2009). PROC QTL - A SAS Procedure for Mapping Quantitative Trait Loci. International Journal of Plant Genomics 2009: 3 doi:10.1155/2009/141234. [PubMed]
Manual: PROC QTL Manual.chm, PROC QTL Manual.pdf, Principles and Procedures of QTL Mapping.pdf
Download: PROC QTL setup.exe
Supplement: Calculation of QTL heritabilities
GLMM for segregation distortion
Download: GLMM for segregation distortion.zip
Genome-wide Evaluation
Pub-Date: October 05, 2010
Introduction:
The program performs genome-wide evaluation under a multiple variance component model by using the maximum likelihood (ML) method and the MCMC implemented Bayesian method. There are two corresponding SAS programs (ML and Bayesian program) and three example data files. To analyze your own field data, slight modification of the programs is required. The necessary modification can be found in the comments of each program code.
Correct citation:
Lide Han and Shizhong Xu (2010). Genome-wide evaluation for quantitative trait loci under the variance component model. Genetica 138:1099-1109 doi: 10.1007/s10709-010-9497-1. [PubMed]
Download: Genome-wide Evaluation.zip
SEM - EQTL
Pub-Date: May 18, 2010
Introduction:
The R program is designed to cluster the association between traits(markers) and transcripts and estimate traits(markers) effects. This program is only for single marker analysis. Each time only one trait(marker) is considered in the algorithm. The associations between the trait(marker) and transcripts are determined by posterior probability pi (see details in paper 'A Stochastic Expectation and Maximization (SEM) Algorithm for Detecting Quantitative Trait Associated Genes'). A data are posted on the website containing 644 transcripts, 150 individual and 495 markers. y is the transcripts file. z is the marker information file.
Download: SEM algorithm for Single Marker.txt, y.csv, z.csv
QXE
Pub-data: April 19, 2010
Introduction:
The R program is designed to estimate QTL main effects and G×E effects for single trait in multiple environments. The program contains four residual error structures: homogeneous diagonal, heterogeneous diagonal, unstructured and factor-analytic matrix. The program can be used to analyze two types of population: backcross and F2. A simulated data is posted on the website including 28 environments, 145 backcross population and 127 markers. In factor analysis of R program, q specifies the number of factors you wish to use.
Download: QE.txt, y.csv, z.csv
MCMC-QTL-NOTE
Pub-Date: Jan 25, 2010
Introduction: Mapping Quantitative Trait Loci Using the MCMC Procedure in SAS
The MCMC procedure in SAS is particularly designed for Bayesian analysis using the Markov chain Monte Carlo (MCMC) algorithm. The program is extremely general to analyze data under any complicated statistical models. This article introduces the MCMC procedure and its application to quantitative trait locus (QTL) mapping. A real life QTL mapping experiment is used as an example to demonstrate the MCMC analysis.
Download: proc mcmc.zip
BayesReview
Pub-Date: Jan 6, 2010
Download: BayesReview.zip, damage.sas, seeds.sas, fertility.sas, fertility.csv, map.csv
EM Lasso
Pub-Date: Oct 26, 2009
Introduction:
There are four SAS programs, three data files and one Readme file. The data are stored in excel spread sheets in comma delimited format.
Manual: doc
Download: zip
QTL-Power
Pub-Date: Feb, 2008
Introduction:
This program is designed to calculate the statistical power for QTL detection using flanking markers for the following types of population, F2, BC, DH and RIL. The program can also calculate the sample size, the marker density, the Type I error and the size of QTL (expressed as proportion of the phenotypic variance explained by the QTL. There are a total of six parameters required for the program: type of population (PopType), Type I error (alpha), sample size (n), size of marker interval (d), and proportion of the phenotypic variance explained by the QTL (h2). The QTL of interest is always assumed to be in the middle of the marker interval, i.e., 0.5d cM away from either marker.
The program can calculate five of the six parameters (excluding population type) given the values of the remaining parameters. Users should provide the input values for the input paramaters and set missing value for the output parameter. The current setting of the program (after the proc iml statement) is to calculate the power for an F2 population given alpha=0.01, n=100, d=10 and h2=0.05. Users can modify the current setting to calculate the parameter of their own interest.
Download: .sas, .zip, .rar, .7z
QTL-by-SAS/SIBPAIR
Pub-Date: Jun, 2004
Introduction:
This is a unified SAS program for single marker analysis and interval mapping of QTL using the extended sib-pair method under the random model methodology.
The SAS macro language is used to implement the mixed model QTL mapping procedure. The program consists of four macros. The first macro qtlibd calculates the proportion of genes IBD shared by sibs at putative QTL positions along each chromosome. This macro handles missing and partially informative markers using the multipoint method. The second macro qtlscan (including qtlscan1 for use of an optimization subroutine of PROC IML and qtlscan2 for use of PROC MIXED) provides estimates of QTL variance components and the likelihood ratio test statistic for any putative positions of the genome. The third macro qtlcritic determines the critical value used to declare statistical significance using the approximate method of Piepho. The last macro qtlplot draws SAS graphs showing the test statistical profiles of the genome.
Shrinkage Mapping
Pub-Date: Nov, 2006
Introduction:
This program performs QTL mapping using hierarchical Bayesian models. It handles three types of experimental populations: backcross, F2, and four-way cross. The trait value can be either continuous or categorical, however, the program does not deal with multiple traits. The program supports both the Windows and the Unix.
Manual: .pdf
E-Bayes QTL
Pub-Date: Sep, 2006
Introduction:
In the Supplementary Material, we provide two data sets and three SAS programs. The two datasets include a simulated one and a real one (barley), both of which were analyzed and reported in the paper. The three SAS programs are EBAYES, SSVS and PENAL. Each of the SAS programs has been customized to run the simulated dataset without making any modification other than to specify the correct directory to store your input and output files. To analyze the barley data or your own data, slight modification of the programs is required. The necessary modification can be found in the comments of each program code.
Manual: .pdf
BMM Differential Analysis
Pub-Date: May, 2003
Introduction:
This program contains a set of SAS macros for QTL interval mapping and composite interval mapping in line crosses by the maximum likelihood method implemented via the EM algorithm.
Manual: .pdf
QTL-by-SAS 1.0
Pub-Date: Nov, 2002
Introduction:
This program contains a set of SAS macros for QTL interval mapping and composite interval mapping in line crosses by the maximum likelihood method implemented via the EM algorithm.